Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector

Saptarshi Sengupta, Ted Pedersen


Abstract
This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one–sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.
Anthology ID:
S19-2162
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
949–953
Language:
URL:
https://aclanthology.org/S19-2162
DOI:
10.18653/v1/S19-2162
Bibkey:
Cite (ACL):
Saptarshi Sengupta and Ted Pedersen. 2019. Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 949–953, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector (Sengupta & Pedersen, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2162.pdf